served from IRI/LDEO Climate Data Library
root mean sq root mean sq ∂Y ∂S detrended-bfl detrended-bfl [ ECMWF S2S ISAC forecast perturbed sfc_precip tp ] : ∂Y ∂S Accumulated Total Precipitation data
forecast perturbed sfc_precip tp adif adif partial_S partial_Y partial_Y ∂Y ∂S Accumulated Total Precipitation from ECMWF S2S ISAC: ISAC-CNR Ensemble.Independent Variables (Grids)
- Latitude (latitude)
- grid: /Y (degree_north) ordered (88.75N) to (88.75S) by 2.5 N= 72 pts :grid
Other Info
- bufferwordsize
- 8
- CE
- null
- CS
- null
- datatype
- doublearraytype
- file_missing_value
- 9.99900026E20
- gribPDSpattern
- 04XXXX000B0134
- missing_value
- NaN
- pointwidth
- 0.0
- standard_name
- precipitation_amount
- units
- 0.000663145596216232 kilogram meter-2 radian-1 north second-1
- history
- root mean sq root mean sq $partialdiff sub Y$ $partialdiff sub S$ detrended-bfl detrended-bfl [ ECMWF S2S ISAC forecast perturbed sfc_precip tp ]
- Averaged over L[0.0 days, 31.0 days] S[9 Nov 2015, 2400 20 Nov 2019] minimum 0.0% data present
Averaged over X[1.253497W, 0.2464905W] M[1, 40] minimum 0.0% data present
- Averaged over L[0.0 days, 31.0 days] S[9 Nov 2015, 2400 20 Nov 2019] minimum 0.0% data present
Last updated: Fri, 13 Dec 2019 18:06:04 GMT
Data Views
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Filters
Here are some filters that are useful for manipulating data. There are actually many more available, but they have to be entered manually. See Ingrid Function Documentation for more information.- Monthly Climatology calculates a monthly climatology by averaging over all years.
- anomalies calculates the difference between the (above) monthly climatology and the original data.
- Integrate along Y
- Differentiate along Y
- Take differences along Y
RMS (root mean square with mean *not* removed) over Y |
RMSA (root mean square with mean removed) over Y |
Maximum over Y |
Minimum over Y |
Detrend (best-fit-line) over Y |
Note on units